Perform dose-effect fitting. A generalized linear model (GLM) is used by default, with a maximum likelihood estimation (MLE) as a fallback method.
Arguments
- count_data
Count data in data frame form.
- model_formula
Model formula.
- model_family
Model family.
- fit_link
Family link.
- aberr_module
Aberration module.
- algorithm
Optional selection of algorithm to be used, either "glm" (for GLM) or "maxlik" (for MLE). By default, "glm" is used, with "maxlik" as a fallback method.
Details
The GLM method is based on the paper by Edwards, A. A. et al. (1979). Radiation induced chromosome aberrations and the Poisson distribution. Radiation and Environmental Biophysics, 16(2), 89-100. <doi:10.1007/BF01323216>.
The MLE method is based on the paperby Oliveira, M. et al. (2016). Zero-inflated regression models for radiation-induced chromosome aberration data: A comparative study. Biometrical Journal, 58(2), 259-279. <doi:10.1002/bimj.201400233>.
Examples
count_data <- data.frame(
D = c(0, 0.1, 0.25, 0.5, 0.75, 1, 1.5, 2, 3, 4, 5),
N = c(5000, 5002, 2008, 2002, 1832, 1168, 562, 333, 193, 103, 59),
X = c(8, 14, 22, 55, 100, 109, 100, 103, 108, 103, 107),
C0 = c(4992, 4988, 1987, 1947, 1736, 1064, 474, 251, 104, 35, 11),
C1 = c(8, 14, 20, 55, 92, 99, 76, 63, 72, 41, 19),
C2 = c(0, 0, 1, 0, 4, 5, 12, 17, 15, 21, 11),
C3 = c(0, 0, 0, 0, 0, 0, 0, 2, 2, 4, 9),
C4 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 6),
C5 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3),
mean = c(0.0016, 0.0028, 0.0110, 0.0275, 0.0546, 0.0933, 0.178, 0.309, 0.560, 1, 1.81),
var = c(0.00160, 0.00279, 0.0118, 0.0267, 0.0560, 0.0933, 0.189, 0.353, 0.466, 0.882, 2.09),
DI = c(0.999, 0.997, 1.08, 0.973, 1.03, 0.999, 1.06, 1.14, 0.834, 0.882, 1.15),
u = c(-0.0748, -0.135, 2.61, -0.861, 0.790, -0.0176, 1.08, 1.82, -1.64, -0.844, 0.811)
)
fit(count_data = count_data,
model_formula = "lin-quad",
model_family = "automatic",
fit_link = "identity",
aberr_module = "dicentrics",
algorithm = "maxlik")
#> $fit_raw_data
#> D N X C0 C1 C2 C3 C4 C5 mean var DI u
#> [1,] 0.00 5000 8 4992 8 0 0 0 0 0.0016 0.00160 0.999 -0.0748
#> [2,] 0.10 5002 14 4988 14 0 0 0 0 0.0028 0.00279 0.997 -0.1350
#> [3,] 0.25 2008 22 1987 20 1 0 0 0 0.0110 0.01180 1.080 2.6100
#> [4,] 0.50 2002 55 1947 55 0 0 0 0 0.0275 0.02670 0.973 -0.8610
#> [5,] 0.75 1832 100 1736 92 4 0 0 0 0.0546 0.05600 1.030 0.7900
#> [6,] 1.00 1168 109 1064 99 5 0 0 0 0.0933 0.09330 0.999 -0.0176
#> [7,] 1.50 562 100 474 76 12 0 0 0 0.1780 0.18900 1.060 1.0800
#> [8,] 2.00 333 103 251 63 17 2 0 0 0.3090 0.35300 1.140 1.8200
#> [9,] 3.00 193 108 104 72 15 2 0 0 0.5600 0.46600 0.834 -1.6400
#> [10,] 4.00 103 103 35 41 21 4 2 0 1.0000 0.88200 0.882 -0.8440
#> [11,] 5.00 59 107 11 19 11 9 6 3 1.8100 2.09000 1.150 0.8110
#>
#> $fit_formula_raw
#> [1] "aberr ~ -1 + coeff_C + coeff_alpha + coeff_beta"
#>
#> $fit_formula_tex
#> [1] "\\lambda = C + \\alpha D + \\beta D^{2}"
#>
#> $fit_coeffs
#> estimate std.error statistic p.value
#> coeff_C 0.001281389 0.0004354802 2.942473 3.727799e-02
#> coeff_alpha 0.021040447 0.0051768738 4.064315 7.223528e-03
#> coeff_beta 0.063026217 0.0040877861 15.418179 6.226154e-07
#>
#> $fit_cor_mat
#> coeff_C coeff_alpha coeff_beta
#> coeff_C 1.0000000 -0.3342101 0.1829917
#> coeff_alpha -0.3342101 1.0000000 -0.7396223
#> coeff_beta 0.1829917 -0.7396223 1.0000000
#>
#> $fit_var_cov_mat
#> coeff_C coeff_alpha coeff_beta
#> coeff_C 1.896430e-07 -7.534521e-07 3.257526e-07
#> coeff_alpha -7.534521e-07 2.680002e-05 -1.565185e-05
#> coeff_beta 3.257526e-07 -1.565185e-05 1.670999e-05
#>
#> $fit_dispersion
#> [1] 1.010601
#>
#> $fit_model_statistics
#> logLik deviance df AIC BIC
#> [1,] -35.71196 6.809766 8 77.42392 78.61761
#>
#> $fit_algorithm
#> [1] "constraint-maxlik-optimization"
#>
#> $fit_model_summary
#> [1] "A quasi-Poisson model accounting for overdispersion was used as the model dispersion (=1.01) > 1."
#>